Overview

Dataset statistics

Number of variables32
Number of observations9379
Missing cells1485
Missing cells (%)0.5%
Duplicate rows744
Duplicate rows (%)7.9%
Total size in memory2.3 MiB
Average record size in memory256.0 B

Variable types

Numeric15
Categorical17

Alerts

Dataset has 744 (7.9%) duplicate rowsDuplicates
Model has a high cardinality: 1304 distinct values High cardinality
SellerName has a high cardinality: 3971 distinct values High cardinality
StreetName has a high cardinality: 3967 distinct values High cardinality
State has a high cardinality: 59 distinct values High cardinality
Zipcode has a high cardinality: 2163 distinct values High cardinality
ExteriorColor has a high cardinality: 948 distinct values High cardinality
InteriorColor has a high cardinality: 373 distinct values High cardinality
Transmission has a high cardinality: 92 distinct values High cardinality
Engine has a high cardinality: 325 distinct values High cardinality
VIN has a high cardinality: 8474 distinct values High cardinality
Stock# has a high cardinality: 8430 distinct values High cardinality
Price is highly correlated with ConsumerReviewsHigh correlation
ConsumerRating is highly correlated with ComfortRating and 5 other fieldsHigh correlation
ConsumerReviews is highly correlated with PriceHigh correlation
ComfortRating is highly correlated with ConsumerRating and 4 other fieldsHigh correlation
InteriorDesignRating is highly correlated with ConsumerRating and 4 other fieldsHigh correlation
PerformanceRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
ValueForMoneyRating is highly correlated with ConsumerRating and 3 other fieldsHigh correlation
ExteriorStylingRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
ReliabilityRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
MinMPG is highly correlated with MaxMPGHigh correlation
MaxMPG is highly correlated with MinMPGHigh correlation
ConsumerRating is highly correlated with ComfortRating and 5 other fieldsHigh correlation
ComfortRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
InteriorDesignRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
PerformanceRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
ValueForMoneyRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
ExteriorStylingRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
ReliabilityRating is highly correlated with ConsumerRating and 5 other fieldsHigh correlation
MinMPG is highly correlated with MaxMPGHigh correlation
MaxMPG is highly correlated with MinMPGHigh correlation
ConsumerRating is highly correlated with ComfortRating and 5 other fieldsHigh correlation
ComfortRating is highly correlated with ConsumerRating and 2 other fieldsHigh correlation
InteriorDesignRating is highly correlated with ConsumerRating and 3 other fieldsHigh correlation
PerformanceRating is highly correlated with ConsumerRating and 4 other fieldsHigh correlation
ValueForMoneyRating is highly correlated with ConsumerRating and 1 other fieldsHigh correlation
ExteriorStylingRating is highly correlated with ConsumerRating and 3 other fieldsHigh correlation
ReliabilityRating is highly correlated with ConsumerRating and 3 other fieldsHigh correlation
MinMPG is highly correlated with MaxMPGHigh correlation
MaxMPG is highly correlated with MinMPGHigh correlation
FuelType is highly correlated with SellerTypeHigh correlation
Transmission is highly correlated with SellerTypeHigh correlation
SellerType is highly correlated with FuelType and 1 other fieldsHigh correlation
Year is highly correlated with Transmission and 1 other fieldsHigh correlation
Make is highly correlated with Price and 13 other fieldsHigh correlation
Price is highly correlated with Make and 1 other fieldsHigh correlation
ConsumerRating is highly correlated with Make and 9 other fieldsHigh correlation
ConsumerReviews is highly correlated with Make and 1 other fieldsHigh correlation
SellerType is highly correlated with FuelType and 1 other fieldsHigh correlation
ComfortRating is highly correlated with Make and 10 other fieldsHigh correlation
InteriorDesignRating is highly correlated with Make and 9 other fieldsHigh correlation
PerformanceRating is highly correlated with Make and 7 other fieldsHigh correlation
ValueForMoneyRating is highly correlated with Make and 9 other fieldsHigh correlation
ExteriorStylingRating is highly correlated with Make and 9 other fieldsHigh correlation
ReliabilityRating is highly correlated with Make and 9 other fieldsHigh correlation
Drivetrain is highly correlated with MakeHigh correlation
MinMPG is highly correlated with Make and 9 other fieldsHigh correlation
MaxMPG is highly correlated with Make and 10 other fieldsHigh correlation
FuelType is highly correlated with Make and 4 other fieldsHigh correlation
Transmission is highly correlated with Year and 13 other fieldsHigh correlation
Mileage is highly correlated with Year and 1 other fieldsHigh correlation
DealType has 222 (2.4%) missing values Missing
InteriorColor has 1088 (11.6%) missing values Missing
VIN is uniformly distributed Uniform
Stock# is uniformly distributed Uniform

Reproduction

Analysis started2022-08-25 16:46:24.957849
Analysis finished2022-08-25 16:46:39.088433
Duration14.13 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.721719
Minimum2001
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:39.117759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2015
Q12018
median2019
Q32020
95-th percentile2021
Maximum2022
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.221708122
Coefficient of variation (CV)0.001100551949
Kurtosis9.874396132
Mean2018.721719
Median Absolute Deviation (MAD)1
Skewness-2.479679303
Sum18933591
Variance4.93598698
MonotonicityNot monotonic
2022-08-25T23:46:39.154950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20193263
34.8%
20202012
21.5%
20211335
14.2%
20181067
 
11.4%
2017573
 
6.1%
2016332
 
3.5%
2015236
 
2.5%
2014142
 
1.5%
2013106
 
1.1%
202294
 
1.0%
Other values (12)219
 
2.3%
ValueCountFrequency (%)
20011
 
< 0.1%
20021
 
< 0.1%
20037
 
0.1%
20049
 
0.1%
200511
 
0.1%
20068
 
0.1%
200713
0.1%
200815
0.2%
20099
 
0.1%
201032
0.3%
ValueCountFrequency (%)
202294
 
1.0%
20211335
14.2%
20202012
21.5%
20193263
34.8%
20181067
 
11.4%
2017573
 
6.1%
2016332
 
3.5%
2015236
 
2.5%
2014142
 
1.5%
2013106
 
1.1%

Make
Categorical

HIGH CORRELATION

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
BMW
944 
Mercedes-Benz
810 
Toyota
797 
Honda
713 
Ford
580 
Other values (36)
5535 

Length

Max length13
Median length10
Mean length5.878131997
Min length3

Characters and Unicode

Total characters55131
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowToyota
2nd rowFord
3rd rowRAM
4th rowHonda
5th rowLexus

Common Values

ValueCountFrequency (%)
BMW944
 
10.1%
Mercedes-Benz810
 
8.6%
Toyota797
 
8.5%
Honda713
 
7.6%
Ford580
 
6.2%
Jeep495
 
5.3%
Lexus484
 
5.2%
Audi424
 
4.5%
Chevrolet416
 
4.4%
Subaru310
 
3.3%
Other values (31)3406
36.3%

Length

2022-08-25T23:46:39.193549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bmw944
 
10.1%
mercedes-benz810
 
8.6%
toyota797
 
8.5%
honda713
 
7.6%
ford580
 
6.2%
jeep495
 
5.3%
lexus484
 
5.2%
audi424
 
4.5%
chevrolet416
 
4.4%
subaru310
 
3.3%
Other values (31)3406
36.3%

Most occurring characters

ValueCountFrequency (%)
e6258
 
11.4%
a4406
 
8.0%
o4156
 
7.5%
d3562
 
6.5%
r2626
 
4.8%
n2584
 
4.7%
M2396
 
4.3%
s2388
 
4.3%
u2138
 
3.9%
B1874
 
3.4%
Other values (34)22743
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39921
72.4%
Uppercase Letter14400
 
26.1%
Dash Punctuation810
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6258
15.7%
a4406
11.0%
o4156
10.4%
d3562
8.9%
r2626
 
6.6%
n2584
 
6.5%
s2388
 
6.0%
u2138
 
5.4%
i1787
 
4.5%
c1714
 
4.3%
Other values (14)8302
20.8%
Uppercase Letter
ValueCountFrequency (%)
M2396
16.6%
B1874
13.0%
T1201
8.3%
C1004
 
7.0%
W944
 
6.6%
H925
 
6.4%
I876
 
6.1%
A840
 
5.8%
F799
 
5.5%
L792
 
5.5%
Other values (9)2749
19.1%
Dash Punctuation
ValueCountFrequency (%)
-810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54321
98.5%
Common810
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6258
 
11.5%
a4406
 
8.1%
o4156
 
7.7%
d3562
 
6.6%
r2626
 
4.8%
n2584
 
4.8%
M2396
 
4.4%
s2388
 
4.4%
u2138
 
3.9%
B1874
 
3.4%
Other values (33)21933
40.4%
Common
ValueCountFrequency (%)
-810
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII55131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e6258
 
11.4%
a4406
 
8.0%
o4156
 
7.5%
d3562
 
6.5%
r2626
 
4.8%
n2584
 
4.7%
M2396
 
4.3%
s2388
 
4.3%
u2138
 
3.9%
B1874
 
3.4%
Other values (34)22743
41.3%

Model
Categorical

HIGH CARDINALITY

Distinct1304
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
Grand Cherokee Limited
 
266
CR-V EX-L
 
189
X5 xDrive40i
 
164
XT5 Luxury
 
142
RAV4 LE
 
112
Other values (1299)
8506 

Length

Max length53
Median length44
Mean length14.12911824
Min length2

Characters and Unicode

Total characters132517
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique492 ?
Unique (%)5.2%

Sample

1st rowSienna SE
2nd rowF-150 Lariat
3rd row1500 Laramie
4th rowAccord Sport SE
5th rowRX 350

Common Values

ValueCountFrequency (%)
Grand Cherokee Limited266
 
2.8%
CR-V EX-L189
 
2.0%
X5 xDrive40i164
 
1.7%
XT5 Luxury142
 
1.5%
RAV4 LE112
 
1.2%
GX 460 Base111
 
1.2%
X3 xDrive30i108
 
1.2%
Pilot EX-L102
 
1.1%
CX-5 Grand Touring99
 
1.1%
1500 Laramie88
 
0.9%
Other values (1294)7998
85.3%

Length

2022-08-25T23:46:39.244902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
base883
 
3.6%
limited759
 
3.1%
premium657
 
2.6%
350493
 
2.0%
4matic477
 
1.9%
grand471
 
1.9%
cherokee410
 
1.7%
ex-l383
 
1.5%
se341
 
1.4%
300328
 
1.3%
Other values (734)19628
79.0%

Most occurring characters

ValueCountFrequency (%)
15451
 
11.7%
e9700
 
7.3%
r7141
 
5.4%
i7008
 
5.3%
a6294
 
4.7%
o4487
 
3.4%
L4142
 
3.1%
03990
 
3.0%
n3953
 
3.0%
u3462
 
2.6%
Other values (60)66889
50.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter67403
50.9%
Uppercase Letter33185
25.0%
Space Separator15451
 
11.7%
Decimal Number13765
 
10.4%
Dash Punctuation1635
 
1.2%
Other Punctuation1058
 
0.8%
Open Punctuation8
 
< 0.1%
Close Punctuation8
 
< 0.1%
Math Symbol4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L4142
12.5%
C3172
9.6%
T3136
9.5%
X3091
9.3%
S2959
8.9%
E2825
 
8.5%
R2168
 
6.5%
P1671
 
5.0%
A1521
 
4.6%
D1338
 
4.0%
Other values (16)7162
21.6%
Lowercase Letter
ValueCountFrequency (%)
e9700
14.4%
r7141
10.6%
i7008
10.4%
a6294
 
9.3%
o4487
 
6.7%
n3953
 
5.9%
u3462
 
5.1%
s3302
 
4.9%
t3206
 
4.8%
m3009
 
4.5%
Other values (15)15841
23.5%
Decimal Number
ValueCountFrequency (%)
03990
29.0%
52833
20.6%
32195
15.9%
41753
12.7%
2971
 
7.1%
1661
 
4.8%
6618
 
4.5%
8324
 
2.4%
7268
 
1.9%
9152
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.893
84.4%
/149
 
14.1%
&14
 
1.3%
!2
 
0.2%
Space Separator
ValueCountFrequency (%)
15451
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1635
100.0%
Open Punctuation
ValueCountFrequency (%)
(8
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%
Math Symbol
ValueCountFrequency (%)
+4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin100588
75.9%
Common31929
 
24.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9700
 
9.6%
r7141
 
7.1%
i7008
 
7.0%
a6294
 
6.3%
o4487
 
4.5%
L4142
 
4.1%
n3953
 
3.9%
u3462
 
3.4%
s3302
 
3.3%
t3206
 
3.2%
Other values (41)47893
47.6%
Common
ValueCountFrequency (%)
15451
48.4%
03990
 
12.5%
52833
 
8.9%
32195
 
6.9%
41753
 
5.5%
-1635
 
5.1%
2971
 
3.0%
.893
 
2.8%
1661
 
2.1%
6618
 
1.9%
Other values (9)929
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII132517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15451
 
11.7%
e9700
 
7.3%
r7141
 
5.4%
i7008
 
5.3%
a6294
 
4.7%
o4487
 
3.4%
L4142
 
3.1%
03990
 
3.0%
n3953
 
3.0%
u3462
 
2.6%
Other values (60)66889
50.5%

Used/New
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
Used
7900 
Certified
1479 

Length

Max length9
Median length4
Mean length4.788463589
Min length4

Characters and Unicode

Total characters44911
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUsed
2nd rowUsed
3rd rowUsed
4th rowUsed
5th rowUsed

Common Values

ValueCountFrequency (%)
Used7900
84.2%
Certified1479
 
15.8%

Length

2022-08-25T23:46:39.286636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T23:46:39.327826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
used7900
84.2%
certified1479
 
15.8%

Most occurring characters

ValueCountFrequency (%)
e10858
24.2%
d9379
20.9%
U7900
17.6%
s7900
17.6%
i2958
 
6.6%
C1479
 
3.3%
r1479
 
3.3%
t1479
 
3.3%
f1479
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35532
79.1%
Uppercase Letter9379
 
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10858
30.6%
d9379
26.4%
s7900
22.2%
i2958
 
8.3%
r1479
 
4.2%
t1479
 
4.2%
f1479
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
U7900
84.2%
C1479
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Latin44911
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10858
24.2%
d9379
20.9%
U7900
17.6%
s7900
17.6%
i2958
 
6.6%
C1479
 
3.3%
r1479
 
3.3%
t1479
 
3.3%
f1479
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII44911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10858
24.2%
d9379
20.9%
U7900
17.6%
s7900
17.6%
i2958
 
6.6%
C1479
 
3.3%
r1479
 
3.3%
t1479
 
3.3%
f1479
 
3.3%

Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct5063
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39832.30472
Minimum2300
Maximum449996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:39.365262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2300
5-th percentile18857.4
Q128994
median35999
Q345996.5
95-th percentile69900
Maximum449996
Range447696
Interquartile range (IQR)17002.5

Descriptive statistics

Standard deviation20789.12121
Coefficient of variation (CV)0.5219161019
Kurtosis63.96064894
Mean39832.30472
Median Absolute Deviation (MAD)8096
Skewness5.487239369
Sum373587186
Variance432187560.7
MonotonicityNot monotonic
2022-08-25T23:46:39.409962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2999536
 
0.4%
4299830
 
0.3%
3199829
 
0.3%
3899829
 
0.3%
3999828
 
0.3%
3999526
 
0.3%
3399526
 
0.3%
3599826
 
0.3%
3399825
 
0.3%
3299524
 
0.3%
Other values (5053)9100
97.0%
ValueCountFrequency (%)
23001
< 0.1%
39952
< 0.1%
39991
< 0.1%
42502
< 0.1%
43881
< 0.1%
44981
< 0.1%
47501
< 0.1%
49711
< 0.1%
49951
< 0.1%
49981
< 0.1%
ValueCountFrequency (%)
4499961
< 0.1%
4099991
< 0.1%
3499951
< 0.1%
3098881
< 0.1%
2850002
< 0.1%
2849901
< 0.1%
2799501
< 0.1%
2744351
< 0.1%
2699982
< 0.1%
2690001
< 0.1%

ConsumerRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.702825461
Minimum2.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:39.446317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile4.3
Q14.7
median4.8
Q34.8
95-th percentile4.9
Maximum5
Range2.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.2407945026
Coefficient of variation (CV)0.05120209213
Kurtosis16.54530135
Mean4.702825461
Median Absolute Deviation (MAD)0.1
Skewness-3.298630445
Sum44107.8
Variance0.05798199246
MonotonicityNot monotonic
2022-08-25T23:46:39.485833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4.83497
37.3%
4.72182
23.3%
4.91211
 
12.9%
4.6948
 
10.1%
4.5430
 
4.6%
5336
 
3.6%
4.3236
 
2.5%
4106
 
1.1%
4.1104
 
1.1%
4.494
 
1.0%
Other values (13)235
 
2.5%
ValueCountFrequency (%)
2.52
 
< 0.1%
2.72
 
< 0.1%
2.910
 
0.1%
326
0.3%
3.11
 
< 0.1%
3.31
 
< 0.1%
3.413
 
0.1%
3.522
0.2%
3.67
 
0.1%
3.738
0.4%
ValueCountFrequency (%)
5336
 
3.6%
4.91211
 
12.9%
4.83497
37.3%
4.72182
23.3%
4.6948
 
10.1%
4.5430
 
4.6%
4.494
 
1.0%
4.3236
 
2.5%
4.294
 
1.0%
4.1104
 
1.1%

ConsumerReviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct320
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.1870135
Minimum1
Maximum817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:39.527666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q130
median75
Q3182
95-th percentile540
Maximum817
Range816
Interquartile range (IQR)152

Descriptive statistics

Standard deviation154.9856397
Coefficient of variation (CV)1.163669307
Kurtosis4.3538557
Mean133.1870135
Median Absolute Deviation (MAD)56
Skewness2.058358624
Sum1249161
Variance24020.54852
MonotonicityNot monotonic
2022-08-25T23:46:39.575858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
540234
 
2.5%
10162
 
1.7%
16156
 
1.7%
42144
 
1.5%
102136
 
1.5%
94136
 
1.5%
55132
 
1.4%
224131
 
1.4%
24126
 
1.3%
17125
 
1.3%
Other values (310)7897
84.2%
ValueCountFrequency (%)
169
0.7%
295
1.0%
3100
1.1%
484
0.9%
560
 
0.6%
637
 
0.4%
752
 
0.6%
877
0.8%
935
 
0.4%
10162
1.7%
ValueCountFrequency (%)
81722
 
0.2%
80312
 
0.1%
7812
 
< 0.1%
77081
0.9%
7485
 
0.1%
7437
 
0.1%
7086
 
0.1%
65219
 
0.2%
6353
 
< 0.1%
6239
 
0.1%

SellerType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
Dealer
9339 
Private
 
40

Length

Max length7
Median length6
Mean length6.004264847
Min length6

Characters and Unicode

Total characters56314
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDealer
2nd rowDealer
3rd rowDealer
4th rowDealer
5th rowDealer

Common Values

ValueCountFrequency (%)
Dealer9339
99.6%
Private40
 
0.4%

Length

2022-08-25T23:46:39.615953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T23:46:39.648607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
dealer9339
99.6%
private40
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e18718
33.2%
a9379
16.7%
r9379
16.7%
D9339
16.6%
l9339
16.6%
P40
 
0.1%
i40
 
0.1%
v40
 
0.1%
t40
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46935
83.3%
Uppercase Letter9379
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e18718
39.9%
a9379
20.0%
r9379
20.0%
l9339
19.9%
i40
 
0.1%
v40
 
0.1%
t40
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
D9339
99.6%
P40
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin56314
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e18718
33.2%
a9379
16.7%
r9379
16.7%
D9339
16.6%
l9339
16.6%
P40
 
0.1%
i40
 
0.1%
v40
 
0.1%
t40
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII56314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e18718
33.2%
a9379
16.7%
r9379
16.7%
D9339
16.6%
l9339
16.6%
P40
 
0.1%
i40
 
0.1%
v40
 
0.1%
t40
 
0.1%

SellerName
Categorical

HIGH CARDINALITY

Distinct3971
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
EchoPark Automotive Greenville
 
36
Autos of Dallas
 
35
EchoPark Automotive Salt Lake City Delivery Center
 
33
Principle Volvo Cars San Antonio
 
25
Mungenast St. Louis Honda
 
19
Other values (3966)
9231 

Length

Max length88
Median length72
Mean length23.75391833
Min length1

Characters and Unicode

Total characters222788
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1976 ?
Unique (%)21.1%

Sample

1st rowCarMax Murrieta - Now offering Curbside Pickup and Home Delivery
2nd rowGiant Chevrolet
3rd rowGill Auto Group Madera
4th rowAutoSavvy Las Vegas
5th rowLexus of Henderson

Common Values

ValueCountFrequency (%)
EchoPark Automotive Greenville36
 
0.4%
Autos of Dallas35
 
0.4%
EchoPark Automotive Salt Lake City Delivery Center33
 
0.4%
Principle Volvo Cars San Antonio25
 
0.3%
Mungenast St. Louis Honda19
 
0.2%
Ricart Used Car Factory18
 
0.2%
Mercedes-Benz of Houston North18
 
0.2%
Mercedes-Benz of Pembroke Pines17
 
0.2%
EchoPark Automotive New Orleans Delivery Center17
 
0.2%
House of Imports17
 
0.2%
Other values (3961)9144
97.5%

Length

2022-08-25T23:46:39.689488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of2411
 
7.0%
honda651
 
1.9%
toyota642
 
1.9%
ford570
 
1.7%
chrysler557
 
1.6%
bmw550
 
1.6%
auto547
 
1.6%
jeep546
 
1.6%
dodge531
 
1.5%
chevrolet522
 
1.5%
Other values (2595)26874
78.1%

Most occurring characters

ValueCountFrequency (%)
25063
 
11.2%
e18452
 
8.3%
o18178
 
8.2%
a15040
 
6.8%
r13658
 
6.1%
n10486
 
4.7%
t10057
 
4.5%
i9918
 
4.5%
l9111
 
4.1%
s8224
 
3.7%
Other values (63)84601
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter158813
71.3%
Uppercase Letter37079
 
16.6%
Space Separator25063
 
11.2%
Dash Punctuation980
 
0.4%
Other Punctuation613
 
0.3%
Decimal Number150
 
0.1%
Open Punctuation45
 
< 0.1%
Close Punctuation45
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e18452
11.6%
o18178
11.4%
a15040
 
9.5%
r13658
 
8.6%
n10486
 
6.6%
t10057
 
6.3%
i9918
 
6.2%
l9111
 
5.7%
s8224
 
5.2%
u6840
 
4.3%
Other values (16)38849
24.5%
Uppercase Letter
ValueCountFrequency (%)
C4528
 
12.2%
M3793
 
10.2%
A3179
 
8.6%
B2538
 
6.8%
S2332
 
6.3%
H2133
 
5.8%
N2079
 
5.6%
P1679
 
4.5%
L1649
 
4.4%
D1608
 
4.3%
Other values (16)11561
31.2%
Decimal Number
ValueCountFrequency (%)
140
26.7%
225
16.7%
621
14.0%
421
14.0%
511
 
7.3%
010
 
6.7%
39
 
6.0%
87
 
4.7%
95
 
3.3%
71
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.227
37.0%
'162
26.4%
,112
18.3%
&70
 
11.4%
/31
 
5.1%
#10
 
1.6%
!1
 
0.2%
Space Separator
ValueCountFrequency (%)
25063
100.0%
Dash Punctuation
ValueCountFrequency (%)
-980
100.0%
Open Punctuation
ValueCountFrequency (%)
(45
100.0%
Close Punctuation
ValueCountFrequency (%)
)45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin195892
87.9%
Common26896
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e18452
 
9.4%
o18178
 
9.3%
a15040
 
7.7%
r13658
 
7.0%
n10486
 
5.4%
t10057
 
5.1%
i9918
 
5.1%
l9111
 
4.7%
s8224
 
4.2%
u6840
 
3.5%
Other values (42)75928
38.8%
Common
ValueCountFrequency (%)
25063
93.2%
-980
 
3.6%
.227
 
0.8%
'162
 
0.6%
,112
 
0.4%
&70
 
0.3%
(45
 
0.2%
)45
 
0.2%
140
 
0.1%
/31
 
0.1%
Other values (11)121
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII222788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25063
 
11.2%
e18452
 
8.3%
o18178
 
8.2%
a15040
 
6.8%
r13658
 
6.1%
n10486
 
4.7%
t10057
 
4.5%
i9918
 
4.5%
l9111
 
4.1%
s8224
 
3.7%
Other values (63)84601
38.0%

SellerRating
Real number (ℝ≥0)

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.412570637
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:39.736275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14.3
median4.6
Q34.8
95-th percentile4.9
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.6262584193
Coefficient of variation (CV)0.1419259817
Kurtosis8.665780286
Mean4.412570637
Median Absolute Deviation (MAD)0.2
Skewness-2.712971004
Sum41385.5
Variance0.3921996078
MonotonicityNot monotonic
2022-08-25T23:46:39.780436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
4.71671
17.8%
4.81513
16.1%
4.61261
13.4%
4.5973
10.4%
4.9863
9.2%
4.4545
 
5.8%
4.3467
 
5.0%
4.2277
 
3.0%
4189
 
2.0%
4.1178
 
1.9%
Other values (30)1442
15.4%
ValueCountFrequency (%)
147
0.5%
1.11
 
< 0.1%
1.321
0.2%
1.44
 
< 0.1%
1.54
 
< 0.1%
1.617
 
0.2%
1.75
 
0.1%
1.824
0.3%
1.98
 
0.1%
234
0.4%
ValueCountFrequency (%)
5174
 
1.9%
4.9863
9.2%
4.81513
16.1%
4.71671
17.8%
4.61261
13.4%
4.5973
10.4%
4.4545
 
5.8%
4.3467
 
5.0%
4.2277
 
3.0%
4.1178
 
1.9%

SellerReviews
Real number (ℝ≥0)

Distinct1738
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean984.0899883
Minimum1
Maximum27824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:39.823896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q1112
median542
Q31272
95-th percentile3441
Maximum27824
Range27823
Interquartile range (IQR)1160

Descriptive statistics

Standard deviation1609.039864
Coefficient of variation (CV)1.635053585
Kurtosis88.65606591
Mean984.0899883
Median Absolute Deviation (MAD)485
Skewness6.919107539
Sum9229780
Variance2589009.283
MonotonicityNot monotonic
2022-08-25T23:46:39.866956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2118
 
1.3%
4113
 
1.2%
1112
 
1.2%
392
 
1.0%
665
 
0.7%
763
 
0.7%
1259
 
0.6%
1350
 
0.5%
3947
 
0.5%
1043
 
0.5%
Other values (1728)8617
91.9%
ValueCountFrequency (%)
1112
1.2%
2118
1.3%
392
1.0%
4113
1.2%
538
 
0.4%
665
0.7%
763
0.7%
842
 
0.4%
939
 
0.4%
1043
 
0.5%
ValueCountFrequency (%)
278247
0.1%
273513
 
< 0.1%
141241
 
< 0.1%
129129
0.1%
127758
0.1%
117581
 
< 0.1%
115133
 
< 0.1%
96952
 
< 0.1%
96931
 
< 0.1%
94692
 
< 0.1%

StreetName
Categorical

HIGH CARDINALITY

Distinct3967
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
2930 Laurens Rd Greenville
 
36
4472 W Plano Pkwy Plano
 
35
3638 South State St Salt Lake City
 
33
1326 NE Interstate 410 Loop San Antonio
 
25
5939 S Lindbergh Blvd Saint Louis
 
19
Other values (3962)
9231 

Length

Max length81
Median length43
Mean length27.43074955
Min length4

Characters and Unicode

Total characters257273
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1976 ?
Unique (%)21.1%

Sample

1st row25560 Madison Ave Murrieta
2nd row1001 S Ben Maddox Way Visalia
3rd row1100 S Madera Ave Madera
4th row2121 E Sahara Ave Las Vegas
5th row7737 Eastgate Rd Henderson

Common Values

ValueCountFrequency (%)
2930 Laurens Rd Greenville36
 
0.4%
4472 W Plano Pkwy Plano35
 
0.4%
3638 South State St Salt Lake City33
 
0.4%
1326 NE Interstate 410 Loop San Antonio25
 
0.3%
5939 S Lindbergh Blvd Saint Louis19
 
0.2%
17510 I-45 North Freeway Houston18
 
0.2%
4255 S Hamilton Rd Groveport18
 
0.2%
Online Only Metairie17
 
0.2%
14199 Pines Blvd Pembroke Pines17
 
0.2%
4500 Stevens Creek Blvd San Jose17
 
0.2%
Other values (3957)9144
97.5%

Length

2022-08-25T23:46:39.924882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rd1621
 
3.6%
ave1132
 
2.5%
st1080
 
2.4%
blvd987
 
2.2%
w824
 
1.8%
e725
 
1.6%
s697
 
1.5%
n638
 
1.4%
hwy604
 
1.3%
dr603
 
1.3%
Other values (5375)36380
80.3%

Most occurring characters

ValueCountFrequency (%)
35932
 
14.0%
e16593
 
6.4%
a14215
 
5.5%
o11684
 
4.5%
r11386
 
4.4%
t11189
 
4.3%
n10910
 
4.2%
l10778
 
4.2%
i9521
 
3.7%
07981
 
3.1%
Other values (62)117084
45.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter143068
55.6%
Decimal Number39829
 
15.5%
Uppercase Letter37098
 
14.4%
Space Separator35932
 
14.0%
Other Punctuation900
 
0.3%
Dash Punctuation442
 
0.2%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e16593
11.6%
a14215
9.9%
o11684
 
8.2%
r11386
 
8.0%
t11189
 
7.8%
n10910
 
7.6%
l10778
 
7.5%
i9521
 
6.7%
d6587
 
4.6%
s6580
 
4.6%
Other values (16)33625
23.5%
Uppercase Letter
ValueCountFrequency (%)
S4636
12.5%
R3043
 
8.2%
B2740
 
7.4%
A2551
 
6.9%
W2422
 
6.5%
C2199
 
5.9%
P2072
 
5.6%
N2060
 
5.6%
H2051
 
5.5%
M1798
 
4.8%
Other values (16)11526
31.1%
Decimal Number
ValueCountFrequency (%)
07981
20.0%
17414
18.6%
54541
11.4%
24003
10.1%
33523
8.8%
43011
 
7.6%
72557
 
6.4%
62488
 
6.2%
92216
 
5.6%
82095
 
5.3%
Other Punctuation
ValueCountFrequency (%)
.821
91.2%
'33
 
3.7%
#31
 
3.4%
/10
 
1.1%
&4
 
0.4%
:1
 
0.1%
Space Separator
ValueCountFrequency (%)
35932
100.0%
Dash Punctuation
ValueCountFrequency (%)
-442
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin180166
70.0%
Common77107
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e16593
 
9.2%
a14215
 
7.9%
o11684
 
6.5%
r11386
 
6.3%
t11189
 
6.2%
n10910
 
6.1%
l10778
 
6.0%
i9521
 
5.3%
d6587
 
3.7%
s6580
 
3.7%
Other values (42)70723
39.3%
Common
ValueCountFrequency (%)
35932
46.6%
07981
 
10.4%
17414
 
9.6%
54541
 
5.9%
24003
 
5.2%
33523
 
4.6%
43011
 
3.9%
72557
 
3.3%
62488
 
3.2%
92216
 
2.9%
Other values (10)3441
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII257273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35932
 
14.0%
e16593
 
6.4%
a14215
 
5.5%
o11684
 
4.5%
r11386
 
4.4%
t11189
 
4.3%
n10910
 
4.2%
l10778
 
4.2%
i9521
 
3.7%
07981
 
3.1%
Other values (62)117084
45.5%

State
Categorical

HIGH CARDINALITY

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
TX
1225 
FL
888 
CA
668 
IL
593 
NY
 
440
Other values (54)
5565 

Length

Max length8
Median length2
Mean length2.004264847
Min length2

Characters and Unicode

Total characters18798
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowNV
5th rowNV

Common Values

ValueCountFrequency (%)
TX1225
 
13.1%
FL888
 
9.5%
CA668
 
7.1%
IL593
 
6.3%
NY440
 
4.7%
GA392
 
4.2%
AZ344
 
3.7%
VA340
 
3.6%
NJ337
 
3.6%
OH329
 
3.5%
Other values (49)3823
40.8%

Length

2022-08-25T23:46:39.966850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx1225
 
13.1%
fl888
 
9.5%
ca668
 
7.1%
il593
 
6.3%
ny440
 
4.7%
ga392
 
4.2%
az344
 
3.7%
va340
 
3.6%
nj337
 
3.6%
oh329
 
3.5%
Other values (49)3823
40.8%

Most occurring characters

ValueCountFrequency (%)
A2713
14.4%
N1812
9.6%
L1604
 
8.5%
T1587
 
8.4%
M1351
 
7.2%
C1317
 
7.0%
I1285
 
6.8%
X1225
 
6.5%
F888
 
4.7%
O739
 
3.9%
Other values (33)4277
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter18751
99.7%
Lowercase Letter32
 
0.2%
Decimal Number11
 
0.1%
Dash Punctuation4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2713
14.5%
N1812
9.7%
L1604
 
8.6%
T1587
 
8.5%
M1351
 
7.2%
C1317
 
7.0%
I1285
 
6.9%
X1225
 
6.5%
F888
 
4.7%
O739
 
3.9%
Other values (15)4230
22.6%
Lowercase Letter
ValueCountFrequency (%)
i6
18.8%
g4
12.5%
n3
9.4%
l3
9.4%
e3
9.4%
a2
 
6.2%
d2
 
6.2%
h2
 
6.2%
c2
 
6.2%
u2
 
6.2%
Other values (2)3
9.4%
Decimal Number
ValueCountFrequency (%)
16
54.5%
02
 
18.2%
21
 
9.1%
61
 
9.1%
91
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18783
99.9%
Common15
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2713
14.4%
N1812
9.6%
L1604
 
8.5%
T1587
 
8.4%
M1351
 
7.2%
C1317
 
7.0%
I1285
 
6.8%
X1225
 
6.5%
F888
 
4.7%
O739
 
3.9%
Other values (27)4262
22.7%
Common
ValueCountFrequency (%)
16
40.0%
-4
26.7%
02
 
13.3%
21
 
6.7%
61
 
6.7%
91
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII18798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2713
14.4%
N1812
9.6%
L1604
 
8.5%
T1587
 
8.4%
M1351
 
7.2%
C1317
 
7.0%
I1285
 
6.8%
X1225
 
6.5%
F888
 
4.7%
O739
 
3.9%
Other values (33)4277
22.8%

Zipcode
Categorical

HIGH CARDINALITY

Distinct2163
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
76051
 
69
75093
 
65
30096
 
63
29607
 
48
75034
 
46
Other values (2158)
9088 

Length

Max length10
Median length5
Mean length4.993176245
Min length1

Characters and Unicode

Total characters46831
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique761 ?
Unique (%)8.1%

Sample

1st row92562
2nd row93292
3rd row93637
4th row89104
5th row89011

Common Values

ValueCountFrequency (%)
7605169
 
0.7%
7509365
 
0.7%
3009663
 
0.7%
2960748
 
0.5%
7503446
 
0.5%
2085243
 
0.5%
6054042
 
0.4%
8526039
 
0.4%
7520938
 
0.4%
8411535
 
0.4%
Other values (2153)8891
94.8%

Length

2022-08-25T23:46:40.001977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7605169
 
0.7%
7509365
 
0.7%
3009663
 
0.7%
2960748
 
0.5%
7503446
 
0.5%
2085243
 
0.5%
6054042
 
0.4%
8526039
 
0.4%
7520938
 
0.4%
8411535
 
0.4%
Other values (2153)8891
94.8%

Most occurring characters

ValueCountFrequency (%)
07789
16.6%
15460
11.7%
35299
11.3%
25279
11.3%
74328
9.2%
44268
9.1%
54183
8.9%
63975
8.5%
83289
7.0%
92917
 
6.2%
Other values (22)44
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46787
99.9%
Lowercase Letter34
 
0.1%
Uppercase Letter10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o5
14.7%
i4
11.8%
l4
11.8%
t3
8.8%
e3
8.8%
s2
 
5.9%
p2
 
5.9%
y2
 
5.9%
a1
 
2.9%
m1
 
2.9%
Other values (7)7
20.6%
Decimal Number
ValueCountFrequency (%)
07789
16.6%
15460
11.7%
35299
11.3%
25279
11.3%
74328
9.3%
44268
9.1%
54183
8.9%
63975
8.5%
83289
7.0%
92917
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
L3
30.0%
C2
20.0%
F2
20.0%
B2
20.0%
S1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common46787
99.9%
Latin44
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o5
 
11.4%
i4
 
9.1%
l4
 
9.1%
t3
 
6.8%
e3
 
6.8%
L3
 
6.8%
C2
 
4.5%
F2
 
4.5%
s2
 
4.5%
p2
 
4.5%
Other values (12)14
31.8%
Common
ValueCountFrequency (%)
07789
16.6%
15460
11.7%
35299
11.3%
25279
11.3%
74328
9.3%
44268
9.1%
54183
8.9%
63975
8.5%
83289
7.0%
92917
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII46831
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07789
16.6%
15460
11.7%
35299
11.3%
25279
11.3%
74328
9.2%
44268
9.1%
54183
8.9%
63975
8.5%
83289
7.0%
92917
 
6.2%
Other values (22)44
 
0.1%

DealType
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing222
Missing (%)2.4%
Memory size73.4 KiB
Good
5524 
Great
2408 
Fair
1225 

Length

Max length5
Median length4
Mean length4.262968221
Min length4

Characters and Unicode

Total characters39036
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreat
2nd rowGood
3rd rowGood
4th rowGood
5th rowFair

Common Values

ValueCountFrequency (%)
Good5524
58.9%
Great2408
25.7%
Fair1225
 
13.1%
(Missing)222
 
2.4%

Length

2022-08-25T23:46:40.039159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T23:46:40.076937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
good5524
60.3%
great2408
26.3%
fair1225
 
13.4%

Most occurring characters

ValueCountFrequency (%)
o11048
28.3%
G7932
20.3%
d5524
14.2%
r3633
 
9.3%
a3633
 
9.3%
e2408
 
6.2%
t2408
 
6.2%
F1225
 
3.1%
i1225
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29879
76.5%
Uppercase Letter9157
 
23.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o11048
37.0%
d5524
18.5%
r3633
 
12.2%
a3633
 
12.2%
e2408
 
8.1%
t2408
 
8.1%
i1225
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
G7932
86.6%
F1225
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin39036
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o11048
28.3%
G7932
20.3%
d5524
14.2%
r3633
 
9.3%
a3633
 
9.3%
e2408
 
6.2%
t2408
 
6.2%
F1225
 
3.1%
i1225
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII39036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o11048
28.3%
G7932
20.3%
d5524
14.2%
r3633
 
9.3%
a3633
 
9.3%
e2408
 
6.2%
t2408
 
6.2%
F1225
 
3.1%
i1225
 
3.1%

ComfortRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.771894658
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:40.115820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.4
Q14.7
median4.8
Q34.9
95-th percentile5
Maximum5
Range2
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.2178217236
Coefficient of variation (CV)0.04564680052
Kurtosis17.85548148
Mean4.771894658
Median Absolute Deviation (MAD)0.1
Skewness-3.324965644
Sum44755.6
Variance0.04744630325
MonotonicityNot monotonic
2022-08-25T23:46:40.153626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4.83318
35.4%
4.92398
25.6%
4.71149
 
12.3%
51016
 
10.8%
4.6698
 
7.4%
4.5243
 
2.6%
4.4189
 
2.0%
4.380
 
0.9%
4.270
 
0.7%
3.966
 
0.7%
Other values (7)152
 
1.6%
ValueCountFrequency (%)
329
 
0.3%
3.511
 
0.1%
3.62
 
< 0.1%
3.728
 
0.3%
3.87
 
0.1%
3.966
0.7%
430
 
0.3%
4.145
0.5%
4.270
0.7%
4.380
0.9%
ValueCountFrequency (%)
51016
 
10.8%
4.92398
25.6%
4.83318
35.4%
4.71149
 
12.3%
4.6698
 
7.4%
4.5243
 
2.6%
4.4189
 
2.0%
4.380
 
0.9%
4.270
 
0.7%
4.145
 
0.5%

InteriorDesignRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.72739098
Minimum2.8
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:40.215146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile4.4
Q14.7
median4.8
Q34.8
95-th percentile5
Maximum5
Range2.2
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1943906429
Coefficient of variation (CV)0.04112006891
Kurtosis8.089348648
Mean4.72739098
Median Absolute Deviation (MAD)0.1
Skewness-2.090418603
Sum44338.2
Variance0.03778772204
MonotonicityNot monotonic
2022-08-25T23:46:40.255102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4.82899
30.9%
4.72218
23.6%
4.91572
16.8%
4.61062
 
11.3%
5551
 
5.9%
4.5468
 
5.0%
4.4171
 
1.8%
4.2149
 
1.6%
4.3100
 
1.1%
496
 
1.0%
Other values (8)93
 
1.0%
ValueCountFrequency (%)
2.82
 
< 0.1%
3.33
 
< 0.1%
3.57
 
0.1%
3.61
 
< 0.1%
3.74
 
< 0.1%
3.842
 
0.4%
3.91
 
< 0.1%
496
1.0%
4.133
 
0.4%
4.2149
1.6%
ValueCountFrequency (%)
5551
 
5.9%
4.91572
16.8%
4.82899
30.9%
4.72218
23.6%
4.61062
 
11.3%
4.5468
 
5.0%
4.4171
 
1.8%
4.3100
 
1.1%
4.2149
 
1.6%
4.133
 
0.4%

PerformanceRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.696289583
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:40.292024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.2
Q14.6
median4.7
Q34.8
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.2536638913
Coefficient of variation (CV)0.05401368182
Kurtosis20.3382931
Mean4.696289583
Median Absolute Deviation (MAD)0.1
Skewness-3.018365806
Sum44046.5
Variance0.06434536975
MonotonicityNot monotonic
2022-08-25T23:46:40.332381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4.82582
27.5%
4.72091
22.3%
4.61223
13.0%
4.91191
12.7%
5814
 
8.7%
4.5492
 
5.2%
4.4258
 
2.8%
4.3183
 
2.0%
4.2148
 
1.6%
4.1147
 
1.6%
Other values (13)250
 
2.7%
ValueCountFrequency (%)
12
 
< 0.1%
2.32
 
< 0.1%
2.610
 
0.1%
31
 
< 0.1%
3.22
 
< 0.1%
3.323
0.2%
3.46
 
0.1%
3.59
 
0.1%
3.610
 
0.1%
3.733
0.4%
ValueCountFrequency (%)
5814
 
8.7%
4.91191
12.7%
4.82582
27.5%
4.72091
22.3%
4.61223
13.0%
4.5492
 
5.2%
4.4258
 
2.8%
4.3183
 
2.0%
4.2148
 
1.6%
4.1147
 
1.6%

ValueForMoneyRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.537082845
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:40.601166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.9
Q14.5
median4.6
Q34.7
95-th percentile4.8
Maximum5
Range4
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.3380981562
Coefficient of variation (CV)0.07451884125
Kurtosis18.46491411
Mean4.537082845
Median Absolute Deviation (MAD)0.1
Skewness-3.393599364
Sum42553.3
Variance0.1143103633
MonotonicityNot monotonic
2022-08-25T23:46:40.641926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4.72338
24.9%
4.62006
21.4%
4.51255
13.4%
4.81091
11.6%
4.4933
 
9.9%
4.3437
 
4.7%
5244
 
2.6%
4.9202
 
2.2%
3.9152
 
1.6%
4.2127
 
1.4%
Other values (17)594
 
6.3%
ValueCountFrequency (%)
12
 
< 0.1%
1.72
 
< 0.1%
228
0.3%
2.210
 
0.1%
2.51
 
< 0.1%
2.714
 
0.1%
2.95
 
0.1%
325
0.3%
3.211
 
0.1%
3.336
0.4%
ValueCountFrequency (%)
5244
 
2.6%
4.9202
 
2.2%
4.81091
11.6%
4.72338
24.9%
4.62006
21.4%
4.51255
13.4%
4.4933
 
9.9%
4.3437
 
4.7%
4.2127
 
1.4%
4.1126
 
1.3%

ExteriorStylingRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.782194264
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:40.681461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.5
Q14.7
median4.8
Q34.9
95-th percentile5
Maximum5
Range2
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1715367925
Coefficient of variation (CV)0.03586989216
Kurtosis11.57795379
Mean4.782194264
Median Absolute Deviation (MAD)0.1
Skewness-2.512183456
Sum44852.2
Variance0.02942487119
MonotonicityNot monotonic
2022-08-25T23:46:40.726101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4.82873
30.6%
4.92646
28.2%
4.71900
20.3%
5808
 
8.6%
4.6528
 
5.6%
4.5324
 
3.5%
4.279
 
0.8%
4.466
 
0.7%
452
 
0.6%
3.847
 
0.5%
Other values (6)56
 
0.6%
ValueCountFrequency (%)
32
 
< 0.1%
3.31
 
< 0.1%
3.77
 
0.1%
3.847
0.5%
3.93
 
< 0.1%
452
0.6%
4.15
 
0.1%
4.279
0.8%
4.338
0.4%
4.466
0.7%
ValueCountFrequency (%)
5808
 
8.6%
4.92646
28.2%
4.82873
30.6%
4.71900
20.3%
4.6528
 
5.6%
4.5324
 
3.5%
4.466
 
0.7%
4.338
 
0.4%
4.279
 
0.8%
4.15
 
0.1%

ReliabilityRating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.681746455
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:40.772762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.1
Q14.6
median4.8
Q34.9
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.3681605978
Coefficient of variation (CV)0.07863744893
Kurtosis36.07735217
Mean4.681746455
Median Absolute Deviation (MAD)0.1
Skewness-4.872364481
Sum43910.1
Variance0.1355422257
MonotonicityNot monotonic
2022-08-25T23:46:40.813704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4.82908
31.0%
4.91781
19.0%
4.71497
16.0%
4.61066
 
11.4%
5636
 
6.8%
4.5432
 
4.6%
4.4214
 
2.3%
4.3191
 
2.0%
4.2136
 
1.5%
4.1108
 
1.2%
Other values (14)410
 
4.4%
ValueCountFrequency (%)
128
 
0.3%
23
 
< 0.1%
2.110
 
0.1%
2.57
 
0.1%
2.88
 
0.1%
2.927
 
0.3%
3.110
 
0.1%
3.314
 
0.1%
3.47
 
0.1%
3.5100
1.1%
ValueCountFrequency (%)
5636
 
6.8%
4.91781
19.0%
4.82908
31.0%
4.71497
16.0%
4.61066
 
11.4%
4.5432
 
4.6%
4.4214
 
2.3%
4.3191
 
2.0%
4.2136
 
1.5%
4.1108
 
1.2%

ExteriorColor
Categorical

HIGH CARDINALITY

Distinct948
Distinct (%)10.2%
Missing91
Missing (%)1.0%
Memory size73.4 KiB
Black
897 
White
 
340
Gray
 
320
Diamond Black
 
200
Black Sapphire Metallic
 
167
Other values (943)
7364 

Length

Max length42
Median length33
Mean length14.93852283
Min length1

Characters and Unicode

Total characters138749
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique356 ?
Unique (%)3.8%

Sample

1st rowRed
2nd rowShadow Black
3rd rowGranite Crystal Clearcoat Metallic
4th rowGray
5th rowEminent White Pearl

Common Values

ValueCountFrequency (%)
Black897
 
9.6%
White340
 
3.6%
Gray320
 
3.4%
Diamond Black200
 
2.1%
Black Sapphire Metallic167
 
1.8%
Silver145
 
1.5%
Modern Steel Metallic144
 
1.5%
Polar White129
 
1.4%
Alpine White125
 
1.3%
Crystal Black Pearl125
 
1.3%
Other values (938)6696
71.4%

Length

2022-08-25T23:46:40.862208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metallic3185
 
15.1%
black2856
 
13.5%
white1699
 
8.0%
gray1020
 
4.8%
pearl978
 
4.6%
silver805
 
3.8%
blue574
 
2.7%
crystal504
 
2.4%
clearcoat396
 
1.9%
red382
 
1.8%
Other values (597)8725
41.3%

Most occurring characters

ValueCountFrequency (%)
l15233
 
11.0%
a14576
 
10.5%
e12769
 
9.2%
11836
 
8.5%
i10870
 
7.8%
t9729
 
7.0%
c8086
 
5.8%
r7323
 
5.3%
M4488
 
3.2%
n3947
 
2.8%
Other values (57)39892
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter105212
75.8%
Uppercase Letter21542
 
15.5%
Space Separator11836
 
8.5%
Dash Punctuation102
 
0.1%
Decimal Number34
 
< 0.1%
Other Punctuation19
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l15233
14.5%
a14576
13.9%
e12769
12.1%
i10870
10.3%
t9729
9.2%
c8086
7.7%
r7323
7.0%
n3947
 
3.8%
k3268
 
3.1%
o3038
 
2.9%
Other values (16)16373
15.6%
Uppercase Letter
ValueCountFrequency (%)
M4488
20.8%
B3936
18.3%
S2316
10.8%
W1731
 
8.0%
G1731
 
8.0%
P1653
 
7.7%
C1452
 
6.7%
D627
 
2.9%
R614
 
2.9%
A434
 
2.0%
Other values (16)2560
11.9%
Decimal Number
ValueCountFrequency (%)
07
20.6%
85
14.7%
25
14.7%
55
14.7%
44
11.8%
34
11.8%
92
 
5.9%
11
 
2.9%
61
 
2.9%
Other Punctuation
ValueCountFrequency (%)
/17
89.5%
!2
 
10.5%
Space Separator
ValueCountFrequency (%)
11836
100.0%
Dash Punctuation
ValueCountFrequency (%)
-102
100.0%
Open Punctuation
ValueCountFrequency (%)
[2
100.0%
Close Punctuation
ValueCountFrequency (%)
]2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin126754
91.4%
Common11995
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l15233
12.0%
a14576
11.5%
e12769
 
10.1%
i10870
 
8.6%
t9729
 
7.7%
c8086
 
6.4%
r7323
 
5.8%
M4488
 
3.5%
n3947
 
3.1%
B3936
 
3.1%
Other values (42)35797
28.2%
Common
ValueCountFrequency (%)
11836
98.7%
-102
 
0.9%
/17
 
0.1%
07
 
0.1%
85
 
< 0.1%
25
 
< 0.1%
55
 
< 0.1%
44
 
< 0.1%
34
 
< 0.1%
!2
 
< 0.1%
Other values (5)8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII138749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l15233
 
11.0%
a14576
 
10.5%
e12769
 
9.2%
11836
 
8.5%
i10870
 
7.8%
t9729
 
7.0%
c8086
 
5.8%
r7323
 
5.3%
M4488
 
3.2%
n3947
 
2.8%
Other values (57)39892
28.8%

InteriorColor
Categorical

HIGH CARDINALITY
MISSING

Distinct373
Distinct (%)4.5%
Missing1088
Missing (%)11.6%
Memory size73.4 KiB
Black
3758 
Jet Black
486 
Gray
420 
Ebony
412 
Charcoal
 
214
Other values (368)
3001 

Length

Max length43
Median length5
Mean length6.915571101
Min length3

Characters and Unicode

Total characters57337
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155 ?
Unique (%)1.9%

Sample

1st rowBlack
2nd rowBlack
3rd rowBlack
4th rowBirch
5th rowBlack

Common Values

ValueCountFrequency (%)
Black3758
40.1%
Jet Black486
 
5.2%
Gray420
 
4.5%
Ebony412
 
4.4%
Charcoal214
 
2.3%
Graphite204
 
2.2%
Beige200
 
2.1%
Ivory110
 
1.2%
Red107
 
1.1%
Parchment99
 
1.1%
Other values (363)2281
24.3%
(Missing)1088
 
11.6%

Length

2022-08-25T23:46:40.909562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black4750
43.4%
ebony623
 
5.7%
gray559
 
5.1%
jet514
 
4.7%
beige487
 
4.4%
431
 
3.9%
charcoal285
 
2.6%
graphite250
 
2.3%
brown238
 
2.2%
red230
 
2.1%
Other values (236)2581
23.6%

Most occurring characters

ValueCountFrequency (%)
a7747
13.5%
B5568
 
9.7%
c5515
 
9.6%
l5449
 
9.5%
k4945
 
8.6%
e3175
 
5.5%
2657
 
4.6%
r2405
 
4.2%
o2163
 
3.8%
n1796
 
3.1%
Other values (44)15917
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter43531
75.9%
Uppercase Letter10645
 
18.6%
Space Separator2657
 
4.6%
Other Punctuation503
 
0.9%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a7747
17.8%
c5515
12.7%
l5449
12.5%
k4945
11.4%
e3175
7.3%
r2405
 
5.5%
o2163
 
5.0%
n1796
 
4.1%
t1745
 
4.0%
i1528
 
3.5%
Other values (15)7063
16.2%
Uppercase Letter
ValueCountFrequency (%)
B5568
52.3%
G904
 
8.5%
E746
 
7.0%
C613
 
5.8%
J543
 
5.1%
S382
 
3.6%
R307
 
2.9%
A224
 
2.1%
I187
 
1.8%
M183
 
1.7%
Other values (15)988
 
9.3%
Other Punctuation
ValueCountFrequency (%)
/500
99.4%
,3
 
0.6%
Space Separator
ValueCountFrequency (%)
2657
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54176
94.5%
Common3161
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a7747
14.3%
B5568
10.3%
c5515
10.2%
l5449
 
10.1%
k4945
 
9.1%
e3175
 
5.9%
r2405
 
4.4%
o2163
 
4.0%
n1796
 
3.3%
t1745
 
3.2%
Other values (40)13668
25.2%
Common
ValueCountFrequency (%)
2657
84.1%
/500
 
15.8%
,3
 
0.1%
-1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII57337
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a7747
13.5%
B5568
 
9.7%
c5515
 
9.6%
l5449
 
9.5%
k4945
 
8.6%
e3175
 
5.5%
2657
 
4.6%
r2405
 
4.2%
o2163
 
3.8%
n1796
 
3.1%
Other values (44)15917
27.8%

Drivetrain
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing7
Missing (%)0.1%
Memory size73.4 KiB
All-wheel Drive
4476 
Front-wheel Drive
2329 
Four-wheel Drive
1549 
Rear-wheel Drive
915 
FWD
 
36
Other values (4)
 
67

Length

Max length17
Median length16
Mean length15.62953478
Min length3

Characters and Unicode

Total characters146480
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFront-wheel Drive
2nd rowFour-wheel Drive
3rd rowFour-wheel Drive
4th rowFront-wheel Drive
5th rowFront-wheel Drive

Common Values

ValueCountFrequency (%)
All-wheel Drive4476
47.7%
Front-wheel Drive2329
24.8%
Four-wheel Drive1549
 
16.5%
Rear-wheel Drive915
 
9.8%
FWD36
 
0.4%
AWD34
 
0.4%
4WD20
 
0.2%
RWD12
 
0.1%
Front Wheel Drive1
 
< 0.1%
(Missing)7
 
0.1%

Length

2022-08-25T23:46:40.949428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T23:46:40.994183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
drive9270
49.7%
all-wheel4476
24.0%
front-wheel2329
 
12.5%
four-wheel1549
 
8.3%
rear-wheel915
 
4.9%
fwd36
 
0.2%
awd34
 
0.2%
4wd20
 
0.1%
rwd12
 
0.1%
front1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e28725
19.6%
l18222
12.4%
r14064
9.6%
D9372
 
6.4%
9271
 
6.3%
v9270
 
6.3%
h9270
 
6.3%
i9270
 
6.3%
-9269
 
6.3%
w9269
 
6.3%
Other values (10)20478
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter109093
74.5%
Uppercase Letter18827
 
12.9%
Space Separator9271
 
6.3%
Dash Punctuation9269
 
6.3%
Decimal Number20
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e28725
26.3%
l18222
16.7%
r14064
12.9%
v9270
 
8.5%
h9270
 
8.5%
i9270
 
8.5%
w9269
 
8.5%
o3879
 
3.6%
n2330
 
2.1%
t2330
 
2.1%
Other values (2)2464
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
D9372
49.8%
A4510
24.0%
F3915
20.8%
R927
 
4.9%
W103
 
0.5%
Space Separator
ValueCountFrequency (%)
9271
100.0%
Dash Punctuation
ValueCountFrequency (%)
-9269
100.0%
Decimal Number
ValueCountFrequency (%)
420
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin127920
87.3%
Common18560
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e28725
22.5%
l18222
14.2%
r14064
11.0%
D9372
 
7.3%
v9270
 
7.2%
h9270
 
7.2%
i9270
 
7.2%
w9269
 
7.2%
A4510
 
3.5%
F3915
 
3.1%
Other values (7)12033
9.4%
Common
ValueCountFrequency (%)
9271
50.0%
-9269
49.9%
420
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII146480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e28725
19.6%
l18222
12.4%
r14064
9.6%
D9372
 
6.4%
9271
 
6.3%
v9270
 
6.3%
h9270
 
6.3%
i9270
 
6.3%
-9269
 
6.3%
w9269
 
6.3%
Other values (10)20478
14.0%

MinMPG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct69
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.75541102
Minimum0
Maximum150
Zeros36
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:41.037595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q118
median20
Q324
95-th percentile30
Maximum150
Range150
Interquartile range (IQR)6

Descriptive statistics

Standard deviation14.81286882
Coefficient of variation (CV)0.6509602838
Kurtosis41.73430017
Mean22.75541102
Median Absolute Deviation (MAD)3
Skewness6.242322572
Sum213423
Variance219.4210826
MonotonicityNot monotonic
2022-08-25T23:46:41.084214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191164
12.4%
20989
10.5%
18936
10.0%
22806
 
8.6%
21766
 
8.2%
17637
 
6.8%
23511
 
5.4%
24445
 
4.7%
25444
 
4.7%
27436
 
4.6%
Other values (59)2245
23.9%
ValueCountFrequency (%)
036
 
0.4%
104
 
< 0.1%
112
 
< 0.1%
1232
 
0.3%
13113
 
1.2%
14192
 
2.0%
15377
4.0%
16425
4.5%
17637
6.8%
18936
10.0%
ValueCountFrequency (%)
15012
 
0.1%
1482
 
< 0.1%
1414
 
< 0.1%
1405
 
0.1%
1362
 
< 0.1%
1342
 
< 0.1%
1324
 
< 0.1%
13131
0.3%
1282
 
< 0.1%
12729
0.3%

MaxMPG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.21654761
Minimum0
Maximum133
Zeros43
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:41.129639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q125
median27
Q331
95-th percentile38
Maximum133
Range133
Interquartile range (IQR)6

Descriptive statistics

Standard deviation12.80978343
Coefficient of variation (CV)0.4384427483
Kurtosis33.84221291
Mean29.21654761
Median Absolute Deviation (MAD)3
Skewness5.350324204
Sum274022
Variance164.0905515
MonotonicityNot monotonic
2022-08-25T23:46:41.176821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
261078
 
11.5%
27865
 
9.2%
25860
 
9.2%
33619
 
6.6%
29595
 
6.3%
28580
 
6.2%
24554
 
5.9%
30544
 
5.8%
22456
 
4.9%
31430
 
4.6%
Other values (55)2798
29.8%
ValueCountFrequency (%)
043
 
0.5%
1411
 
0.1%
156
 
0.1%
162
 
< 0.1%
1777
 
0.8%
18101
 
1.1%
19181
 
1.9%
20149
 
1.6%
21267
2.8%
22456
4.9%
ValueCountFrequency (%)
13312
 
0.1%
1322
 
< 0.1%
1274
 
< 0.1%
1262
 
< 0.1%
1245
 
0.1%
1232
 
< 0.1%
11733
0.4%
1165
 
0.1%
11429
0.3%
11218
0.2%

FuelType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing30
Missing (%)0.3%
Memory size73.4 KiB
Gasoline
8905 
Electric
 
162
E85 Flex Fuel
 
121
Hybrid
 
69
Diesel
 
45
Other values (7)
 
47

Length

Max length29
Median length8
Mean length8.080543374
Min length6

Characters and Unicode

Total characters75545
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowGasoline
2nd rowGasoline
3rd rowGasoline
4th rowGasoline
5th rowGasoline

Common Values

ValueCountFrequency (%)
Gasoline8905
94.9%
Electric162
 
1.7%
E85 Flex Fuel121
 
1.3%
Hybrid69
 
0.7%
Diesel45
 
0.5%
Gasoline Fuel30
 
0.3%
Electric Fuel System5
 
0.1%
Gasoline/Mild Electric Hybrid5
 
0.1%
Flex Fuel Capability3
 
< 0.1%
Flexible Fuel2
 
< 0.1%
Other values (2)2
 
< 0.1%
(Missing)30
 
0.3%

Length

2022-08-25T23:46:41.220409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gasoline8935
92.6%
electric172
 
1.8%
fuel162
 
1.7%
flex124
 
1.3%
e85121
 
1.3%
hybrid74
 
0.8%
diesel46
 
0.5%
system5
 
0.1%
gasoline/mild5
 
0.1%
capability3
 
< 0.1%
Other values (3)4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e9500
12.6%
l9458
12.5%
i9246
12.2%
s8992
11.9%
a8947
11.8%
G8941
11.8%
n8941
11.8%
o8940
11.8%
c346
 
0.5%
302
 
0.4%
Other values (23)1932
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter65336
86.5%
Uppercase Letter9658
 
12.8%
Space Separator302
 
0.4%
Decimal Number242
 
0.3%
Other Punctuation6
 
< 0.1%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e9500
14.5%
l9458
14.5%
i9246
14.2%
s8992
13.8%
a8947
13.7%
n8941
13.7%
o8940
13.7%
c346
 
0.5%
r247
 
0.4%
t181
 
0.3%
Other values (8)538
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
G8941
92.6%
E294
 
3.0%
F288
 
3.0%
H74
 
0.8%
D46
 
0.5%
M5
 
0.1%
S5
 
0.1%
C3
 
< 0.1%
P1
 
< 0.1%
I1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
5121
50.0%
8121
50.0%
Space Separator
ValueCountFrequency (%)
302
100.0%
Other Punctuation
ValueCountFrequency (%)
/6
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin74994
99.3%
Common551
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e9500
12.7%
l9458
12.6%
i9246
12.3%
s8992
12.0%
a8947
11.9%
G8941
11.9%
n8941
11.9%
o8940
11.9%
c346
 
0.5%
E294
 
0.4%
Other values (18)1389
 
1.9%
Common
ValueCountFrequency (%)
302
54.8%
5121
22.0%
8121
22.0%
/6
 
1.1%
-1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII75545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e9500
12.6%
l9458
12.5%
i9246
12.2%
s8992
11.9%
a8947
11.8%
G8941
11.8%
n8941
11.8%
o8940
11.8%
c346
 
0.5%
302
 
0.4%
Other values (23)1932
 
2.6%

Transmission
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct92
Distinct (%)1.0%
Missing4
Missing (%)< 0.1%
Memory size73.4 KiB
8-Speed Automatic
3202 
6-Speed Automatic
1676 
Automatic CVT
1294 
9-Speed Automatic
1015 
10-Speed Automatic
448 
Other values (87)
1740 

Length

Max length50
Median length17
Mean length16.9184
Min length1

Characters and Unicode

Total characters158610
Distinct characters60
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.4%

Sample

1st row8-Speed Automatic
2nd row10-Speed Automatic
3rd row8-Speed Automatic
4th rowAutomatic CVT
5th row8-Speed Automatic

Common Values

ValueCountFrequency (%)
8-Speed Automatic3202
34.1%
6-Speed Automatic1676
17.9%
Automatic CVT1294
13.8%
9-Speed Automatic1015
 
10.8%
10-Speed Automatic448
 
4.8%
Automatic377
 
4.0%
7-Speed Automatic with Auto-Shift368
 
3.9%
7-Speed Automatic245
 
2.6%
1-Speed Automatic180
 
1.9%
5-Speed Automatic162
 
1.7%
Other values (82)408
 
4.4%

Length

2022-08-25T23:46:41.263891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
automatic9171
47.3%
8-speed3324
 
17.2%
6-speed1762
 
9.1%
cvt1313
 
6.8%
9-speed1025
 
5.3%
7-speed633
 
3.3%
with497
 
2.6%
auto-shift472
 
2.4%
10-speed457
 
2.4%
1-speed182
 
0.9%
Other values (64)543
 
2.8%

Most occurring characters

ValueCountFrequency (%)
t19852
12.5%
e15188
 
9.6%
i10263
 
6.5%
10004
 
6.3%
A9728
 
6.1%
o9709
 
6.1%
u9695
 
6.1%
a9359
 
5.9%
c9226
 
5.8%
m9177
 
5.8%
Other values (50)46409
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter110047
69.4%
Uppercase Letter22285
 
14.1%
Space Separator10004
 
6.3%
Decimal Number8080
 
5.1%
Dash Punctuation8069
 
5.1%
Other Punctuation116
 
0.1%
Close Punctuation4
 
< 0.1%
Open Punctuation4
 
< 0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t19852
18.0%
e15188
13.8%
i10263
9.3%
o9709
8.8%
u9695
8.8%
a9359
8.5%
c9226
8.4%
m9177
8.3%
d7572
 
6.9%
p7569
 
6.9%
Other values (14)2437
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A9728
43.7%
S8085
36.3%
T1434
 
6.4%
V1341
 
6.0%
C1317
 
5.9%
E146
 
0.7%
D76
 
0.3%
P71
 
0.3%
M49
 
0.2%
O17
 
0.1%
Other values (10)21
 
0.1%
Decimal Number
ValueCountFrequency (%)
83332
41.2%
61765
21.8%
91033
 
12.8%
1643
 
8.0%
7637
 
7.9%
0461
 
5.7%
5173
 
2.1%
433
 
0.4%
23
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/114
98.3%
,2
 
1.7%
Space Separator
ValueCountFrequency (%)
10004
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8069
100.0%
Close Punctuation
ValueCountFrequency (%)
)4
100.0%
Open Punctuation
ValueCountFrequency (%)
(4
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin132332
83.4%
Common26278
 
16.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t19852
15.0%
e15188
11.5%
i10263
7.8%
A9728
7.4%
o9709
7.3%
u9695
7.3%
a9359
7.1%
c9226
7.0%
m9177
6.9%
S8085
 
6.1%
Other values (34)22050
16.7%
Common
ValueCountFrequency (%)
10004
38.1%
-8069
30.7%
83332
 
12.7%
61765
 
6.7%
91033
 
3.9%
1643
 
2.4%
7637
 
2.4%
0461
 
1.8%
5173
 
0.7%
/114
 
0.4%
Other values (6)47
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII158610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t19852
12.5%
e15188
 
9.6%
i10263
 
6.5%
10004
 
6.3%
A9728
 
6.1%
o9709
 
6.1%
u9695
 
6.1%
a9359
 
5.9%
c9226
 
5.8%
m9177
 
5.8%
Other values (50)46409
29.3%

Engine
Categorical

HIGH CARDINALITY

Distinct325
Distinct (%)3.5%
Missing3
Missing (%)< 0.1%
Memory size73.4 KiB
2.0L I4 16V GDI DOHC Turbo
1629 
1.5L I4 16V GDI DOHC Turbo
 
425
3.6L V6 24V MPFI DOHC
 
423
3.0L I6 24V GDI DOHC Turbo
 
417
3.6L V6 24V GDI DOHC
 
390
Other values (320)
6092 

Length

Max length64
Median length63
Mean length23.04916809
Min length2

Characters and Unicode

Total characters216109
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)1.5%

Sample

1st row3.5L V6 24V PDI DOHC
2nd row3.5L V6 24V PDI DOHC Twin Turbo
3rd row5.7L V8 16V MPFI OHV
4th row1.5L I4 16V GDI DOHC Turbo
5th row3.5L V6 24V PDI DOHC

Common Values

ValueCountFrequency (%)
2.0L I4 16V GDI DOHC Turbo1629
 
17.4%
1.5L I4 16V GDI DOHC Turbo425
 
4.5%
3.6L V6 24V MPFI DOHC423
 
4.5%
3.0L I6 24V GDI DOHC Turbo417
 
4.4%
3.6L V6 24V GDI DOHC390
 
4.2%
3.5L V6 24V PDI DOHC297
 
3.2%
3.5L V6 24V GDI SOHC290
 
3.1%
2.4L I4 16V GDI DOHC230
 
2.5%
2.5L I4 16V PDI DOHC221
 
2.4%
3.5L V6 24V GDI DOHC216
 
2.3%
Other values (315)4838
51.6%

Length

2022-08-25T23:46:41.315838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dohc7562
15.2%
gdi5599
11.2%
16v4660
 
9.3%
i43910
 
7.8%
turbo3760
 
7.5%
24v3356
 
6.7%
v63042
 
6.1%
2.0l2215
 
4.4%
mpfi2128
 
4.3%
3.5l1182
 
2.4%
Other values (256)12493
25.0%

Most occurring characters

ValueCountFrequency (%)
40531
18.8%
D14064
 
6.5%
V13321
 
6.2%
I13096
 
6.1%
69592
 
4.4%
H8992
 
4.2%
.8885
 
4.1%
L8839
 
4.1%
48744
 
4.0%
O8574
 
4.0%
Other values (55)81471
37.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter94043
43.5%
Decimal Number44621
20.6%
Space Separator40531
18.8%
Lowercase Letter27625
 
12.8%
Other Punctuation9091
 
4.2%
Dash Punctuation194
 
0.1%
Open Punctuation3
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r4749
17.2%
u4185
15.1%
o3982
14.4%
b3963
14.3%
e2019
7.3%
i1657
 
6.0%
n1623
 
5.9%
l1236
 
4.5%
d769
 
2.8%
w606
 
2.2%
Other values (15)2836
10.3%
Uppercase Letter
ValueCountFrequency (%)
D14064
15.0%
V13321
14.2%
I13096
13.9%
H8992
9.6%
L8839
9.4%
O8574
9.1%
C8278
8.8%
G5663
6.0%
T4398
 
4.7%
P3001
 
3.2%
Other values (10)5817
6.2%
Decimal Number
ValueCountFrequency (%)
69592
21.5%
48744
19.6%
27930
17.8%
15648
12.7%
34115
9.2%
03542
 
7.9%
53205
 
7.2%
81390
 
3.1%
7397
 
0.9%
958
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.8885
97.7%
/180
 
2.0%
:20
 
0.2%
,5
 
0.1%
@1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(2
66.7%
[1
33.3%
Space Separator
ValueCountFrequency (%)
40531
100.0%
Dash Punctuation
ValueCountFrequency (%)
-194
100.0%
Close Punctuation
ValueCountFrequency (%)
]1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin121668
56.3%
Common94441
43.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
D14064
11.6%
V13321
10.9%
I13096
10.8%
H8992
 
7.4%
L8839
 
7.3%
O8574
 
7.0%
C8278
 
6.8%
G5663
 
4.7%
r4749
 
3.9%
T4398
 
3.6%
Other values (35)31694
26.0%
Common
ValueCountFrequency (%)
40531
42.9%
69592
 
10.2%
.8885
 
9.4%
48744
 
9.3%
27930
 
8.4%
15648
 
6.0%
34115
 
4.4%
03542
 
3.8%
53205
 
3.4%
81390
 
1.5%
Other values (10)859
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII216109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40531
18.8%
D14064
 
6.5%
V13321
 
6.2%
I13096
 
6.1%
69592
 
4.4%
H8992
 
4.2%
.8885
 
4.1%
L8839
 
4.1%
48744
 
4.0%
O8574
 
4.0%
Other values (55)81471
37.7%

VIN
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8474
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
JHLRW2H8XKX023259
 
3
SJKCH5CR8JA052840
 
3
58ABZ1B15KU004958
 
3
KNDPMCAC7L7738001
 
3
2T1BURHE0KC202797
 
3
Other values (8469)
9364 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters159443
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7575 ?
Unique (%)80.8%

Sample

1st row5TDXZ3DC2KS015402
2nd row1FTEW1EG2JFD44217
3rd row1C6RR7VT5HS842283
4th row1HGCV1F49MA038035
5th row2T2AZMAA8LC156270

Common Values

ValueCountFrequency (%)
JHLRW2H8XKX0232593
 
< 0.1%
SJKCH5CR8JA0528403
 
< 0.1%
58ABZ1B15KU0049583
 
< 0.1%
KNDPMCAC7L77380013
 
< 0.1%
2T1BURHE0KC2027973
 
< 0.1%
WA1LABF74HD0212643
 
< 0.1%
WA1LAAF70HD0295342
 
< 0.1%
1GYKNCRS2KZ1611492
 
< 0.1%
JF2SJAEC3JH5905192
 
< 0.1%
JTMWFREV0JD1220622
 
< 0.1%
Other values (8464)9353
99.7%

Length

2022-08-25T23:46:41.356738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jhlrw2h8xkx0232593
 
< 0.1%
58abz1b15ku0049583
 
< 0.1%
kndpmcac7l77380013
 
< 0.1%
2t1burhe0kc2027973
 
< 0.1%
wa1labf74hd0212643
 
< 0.1%
sjkch5cr8ja0528403
 
< 0.1%
2t2zk1ba5fc1553752
 
< 0.1%
5uxku2c59g0n812642
 
< 0.1%
5uxcx4c52kls391842
 
< 0.1%
1fmcu0j99dud500562
 
< 0.1%
Other values (8464)9353
99.7%

Most occurring characters

ValueCountFrequency (%)
112036
 
7.5%
510765
 
6.8%
210172
 
6.4%
09208
 
5.8%
48387
 
5.3%
38278
 
5.2%
67092
 
4.4%
86789
 
4.3%
76495
 
4.1%
96305
 
4.0%
Other values (23)73916
46.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85527
53.6%
Uppercase Letter73916
46.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K6133
 
8.3%
C5577
 
7.5%
A5436
 
7.4%
J5023
 
6.8%
F4945
 
6.7%
B4190
 
5.7%
L4040
 
5.5%
M3653
 
4.9%
G3562
 
4.8%
D3280
 
4.4%
Other values (13)28077
38.0%
Decimal Number
ValueCountFrequency (%)
112036
14.1%
510765
12.6%
210172
11.9%
09208
10.8%
48387
9.8%
38278
9.7%
67092
8.3%
86789
7.9%
76495
7.6%
96305
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common85527
53.6%
Latin73916
46.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
K6133
 
8.3%
C5577
 
7.5%
A5436
 
7.4%
J5023
 
6.8%
F4945
 
6.7%
B4190
 
5.7%
L4040
 
5.5%
M3653
 
4.9%
G3562
 
4.8%
D3280
 
4.4%
Other values (13)28077
38.0%
Common
ValueCountFrequency (%)
112036
14.1%
510765
12.6%
210172
11.9%
09208
10.8%
48387
9.8%
38278
9.7%
67092
8.3%
86789
7.9%
76495
7.6%
96305
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII159443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112036
 
7.5%
510765
 
6.8%
210172
 
6.4%
09208
 
5.8%
48387
 
5.3%
38278
 
5.2%
67092
 
4.4%
86789
 
4.3%
76495
 
4.1%
96305
 
4.0%
Other values (23)73916
46.4%

Stock#
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8430
Distinct (%)90.3%
Missing40
Missing (%)0.4%
Memory size73.4 KiB
220577A
 
4
H16463B
 
3
P9090A
 
3
1L3035A
 
3
U15471
 
3
Other values (8425)
9323 

Length

Max length19
Median length17
Mean length7.124317379
Min length3

Characters and Unicode

Total characters66534
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7531 ?
Unique (%)80.6%

Sample

1st row22998646
2nd row22418A
3rd rowNG277871G
4th row54237
5th rowHDT4181A

Common Values

ValueCountFrequency (%)
220577A4
 
< 0.1%
H16463B3
 
< 0.1%
P9090A3
 
< 0.1%
1L3035A3
 
< 0.1%
U154713
 
< 0.1%
0528403
 
< 0.1%
553613
 
< 0.1%
221522A3
 
< 0.1%
220603A3
 
< 0.1%
T0148342
 
< 0.1%
Other values (8420)9309
99.3%
(Missing)40
 
0.4%

Length

2022-08-25T23:46:41.396528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
220577a4
 
< 0.1%
p9090a3
 
< 0.1%
1l3035a3
 
< 0.1%
u154713
 
< 0.1%
0528403
 
< 0.1%
553613
 
< 0.1%
221522a3
 
< 0.1%
220603a3
 
< 0.1%
h16463b3
 
< 0.1%
ip0011342
 
< 0.1%
Other values (8420)9309
99.7%

Most occurring characters

ValueCountFrequency (%)
27743
11.6%
16495
9.8%
06184
 
9.3%
34882
 
7.3%
54558
 
6.9%
44465
 
6.7%
64205
 
6.3%
84055
 
6.1%
74030
 
6.1%
94023
 
6.0%
Other values (38)15894
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50640
76.1%
Uppercase Letter15790
 
23.7%
Dash Punctuation88
 
0.1%
Lowercase Letter14
 
< 0.1%
Connector Punctuation2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2793
17.7%
P2003
12.7%
L1149
 
7.3%
B1115
 
7.1%
K1010
 
6.4%
C840
 
5.3%
M791
 
5.0%
T692
 
4.4%
U634
 
4.0%
H596
 
3.8%
Other values (16)4167
26.4%
Decimal Number
ValueCountFrequency (%)
27743
15.3%
16495
12.8%
06184
12.2%
34882
9.6%
54558
9.0%
44465
8.8%
64205
8.3%
84055
8.0%
74030
8.0%
94023
7.9%
Lowercase Letter
ValueCountFrequency (%)
e3
21.4%
r2
14.3%
o2
14.3%
d1
 
7.1%
l1
 
7.1%
f1
 
7.1%
m1
 
7.1%
a1
 
7.1%
n1
 
7.1%
c1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
-88
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common50730
76.2%
Latin15804
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2793
17.7%
P2003
12.7%
L1149
 
7.3%
B1115
 
7.1%
K1010
 
6.4%
C840
 
5.3%
M791
 
5.0%
T692
 
4.4%
U634
 
4.0%
H596
 
3.8%
Other values (26)4181
26.5%
Common
ValueCountFrequency (%)
27743
15.3%
16495
12.8%
06184
12.2%
34882
9.6%
54558
9.0%
44465
8.8%
64205
8.3%
84055
8.0%
74030
7.9%
94023
7.9%
Other values (2)90
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII66534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27743
11.6%
16495
9.8%
06184
 
9.3%
34882
 
7.3%
54558
 
6.9%
44465
 
6.7%
64205
 
6.3%
84055
 
6.1%
74030
 
6.1%
94023
 
6.0%
Other values (38)15894
23.9%

Mileage
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7827
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37463.02335
Minimum121
Maximum234114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.4 KiB
2022-08-25T23:46:41.438345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile11523.7
Q118666.5
median32907
Q347698
95-th percentile86639.7
Maximum234114
Range233993
Interquartile range (IQR)29031.5

Descriptive statistics

Standard deviation24970.34257
Coefficient of variation (CV)0.6665330327
Kurtosis5.61614881
Mean37463.02335
Median Absolute Deviation (MAD)14468
Skewness1.820101062
Sum351365696
Variance623518008
MonotonicityNot monotonic
2022-08-25T23:46:41.486048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331486
 
0.1%
109695
 
0.1%
342834
 
< 0.1%
375254
 
< 0.1%
150004
 
< 0.1%
410654
 
< 0.1%
161614
 
< 0.1%
323354
 
< 0.1%
332824
 
< 0.1%
151184
 
< 0.1%
Other values (7817)9336
99.5%
ValueCountFrequency (%)
1211
< 0.1%
1401
< 0.1%
3591
< 0.1%
4151
< 0.1%
5201
< 0.1%
5831
< 0.1%
5971
< 0.1%
7131
< 0.1%
7701
< 0.1%
7861
< 0.1%
ValueCountFrequency (%)
2341141
< 0.1%
2286921
< 0.1%
2270001
< 0.1%
2260001
< 0.1%
2167591
< 0.1%
2130001
< 0.1%
2044402
< 0.1%
2037771
< 0.1%
1997841
< 0.1%
1851561
< 0.1%

Interactions

2022-08-25T23:46:38.050764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:29.079702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:29.983256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:30.532878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:31.193345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:31.790811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:32.400637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:33.069102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:33.668903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-08-25T23:46:38.567724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:29.948935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:30.498583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:31.156762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:31.753582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:32.360131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:33.033402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:33.631158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:34.186756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:34.774965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:35.481107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:36.092839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:36.670576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:37.247732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-08-25T23:46:38.015003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-08-25T23:46:41.526747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-25T23:46:41.627809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-25T23:46:41.728857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-25T23:46:41.823696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-25T23:46:41.916647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-25T23:46:38.660638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-25T23:46:38.849040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-25T23:46:38.972421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-25T23:46:39.035176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

YearMakeModelUsed/NewPriceConsumerRatingConsumerReviewsSellerTypeSellerNameSellerRatingSellerReviewsStreetNameStateZipcodeDealTypeComfortRatingInteriorDesignRatingPerformanceRatingValueForMoneyRatingExteriorStylingRatingReliabilityRatingExteriorColorInteriorColorDrivetrainMinMPGMaxMPGFuelTypeTransmissionEngineVINStock#Mileage
02019ToyotaSienna SEUsed39998.04.645.0DealerCarMax Murrieta - Now offering Curbside Pickup and Home Delivery3.33.025560 Madison Ave MurrietaCA92562Great4.74.64.64.44.64.7RedBlackFront-wheel Drive19.027.0Gasoline8-Speed Automatic3.5L V6 24V PDI DOHC5TDXZ3DC2KS0154022299864629403.0
12018FordF-150 LariatUsed49985.04.8817.0DealerGiant Chevrolet4.8131.01001 S Ben Maddox Way VisaliaCA93292Good4.94.84.84.64.84.7Shadow BlackBlackFour-wheel Drive19.024.0Gasoline10-Speed Automatic3.5L V6 24V PDI DOHC Twin Turbo1FTEW1EG2JFD4421722418A32929.0
22017RAM1500 LaramieUsed41860.04.7495.0DealerGill Auto Group Madera4.6249.01100 S Madera Ave MaderaCA93637Good4.84.74.84.64.84.7Granite Crystal Clearcoat MetallicBlackFour-wheel Drive15.021.0Gasoline8-Speed Automatic5.7L V8 16V MPFI OHV1C6RR7VT5HS842283NG277871G23173.0
32021HondaAccord Sport SEUsed28500.05.036.0DealerAutoSavvy Las Vegas4.6284.02121 E Sahara Ave Las VegasNV89104NaN4.95.04.95.05.05.0GrayNaNFront-wheel Drive29.035.0GasolineAutomatic CVT1.5L I4 16V GDI DOHC Turbo1HGCV1F49MA0380355423710598.0
42020LexusRX 350Used49000.04.876.0DealerLexus of Henderson4.84755.07737 Eastgate Rd HendersonNV89011Good4.94.84.84.74.84.9Eminent White PearlBirchFront-wheel Drive20.027.0Gasoline8-Speed Automatic3.5L V6 24V PDI DOHC2T2AZMAA8LC156270HDT4181A28137.0
52012Toyota4Runner SR5Used23541.04.734.0DealerAutoNation Toyota Hayward4.41071.024773 Mission Blvd HaywardCA94544Fair4.74.64.44.64.94.9BlackNaNRear-wheel Drive17.023.0Gasoline5-Speed Automatic4.0L V6 24V MPFI DOHCJTEZU5JR3C5043790C5043790105469.0
62017HondaHR-V LXUsed20995.04.6200.0DealerDowntown Toyota-Subaru of Oakland4.42695.04145 Broadway OaklandCA94611Great4.64.44.44.54.74.8Modern Steel MetallicBlackFront-wheel Drive28.034.0GasolineAutomatic CVT1.8L I4 16V MPFI SOHC3CZRU5H32HG703897T148010458.0
72014Mercedes-BenzE-Class E 350Used18985.04.8176.0DealerDowntown Toyota-Subaru of Oakland4.42695.04145 Broadway OaklandCA94611Great4.94.84.84.64.94.8Lunar Blue MetallicSaffronRear-wheel Drive21.030.0Gasoline7-Speed Automatic3.5L V6 24V GDI DOHCWDDHF5KB3EA778099224922A58157.0
82021HondaPilot Touring 8-PassengerUsed44299.04.863.0DealerEchoPark Automotive Phoenix4.9237.010555 West Papago Freeway AvondaleAZ85323Good4.94.74.84.74.74.8Platinum White PearlBeigeAll-wheel Drive19.026.0Gasoline9-Speed Automatic3.5L V6 24V GDI SOHC5FNYF6H90MB052856YMB05285614445.0
92020DodgeCharger Scat PackCertified46773.04.856.0DealerBill Luke Chrysler Jeep Dodge RAM4.31366.02425 W Camelback Rd PhoenixAZ85015Good4.94.84.94.74.94.9Triple Nickel ClearcoatBlackRear-wheel Drive15.024.0Gasoline8-Speed Automatic6.4L V8 16V MPFI OHV2C3CDXGJ8LH157532CBY70525642.0

Last rows

YearMakeModelUsed/NewPriceConsumerRatingConsumerReviewsSellerTypeSellerNameSellerRatingSellerReviewsStreetNameStateZipcodeDealTypeComfortRatingInteriorDesignRatingPerformanceRatingValueForMoneyRatingExteriorStylingRatingReliabilityRatingExteriorColorInteriorColorDrivetrainMinMPGMaxMPGFuelTypeTransmissionEngineVINStock#Mileage
93692019DodgeDurango GTUsed35500.04.761.0DealerEastchester Chrysler Jeep Dodge RAM4.46302.04007 Boston Rd BronxNY10466Great4.84.74.74.64.74.6Octane Red PearlcoatBlackAll-wheel Drive18.025.0Gasoline8-Speed Automatic3.6L V6 24V MPFI DOHC1C4RDJDG0KC803725EUKC80372517069.0
93702019HondaCR-V EX-LUsed33277.04.8540.0DealerMillennium Honda4.74705.0286 N Franklin St HempsteadNY11550Good4.84.84.74.74.84.8Platinum White PearlBlackAll-wheel Drive27.033.0GasolineAutomatic CVT1.5L I4 16V GDI DOHC Turbo7FARW2H81KE025123U34582T18025.0
93712019SubaruCrosstrek 2.0i LimitedUsed30000.04.7205.0DealerGaravel Subaru4.95447.010 Tindall Ave NorwalkCT06851Good4.74.74.64.74.84.8Crystal White PearlGrayAll-wheel Drive27.033.0GasolineAutomatic CVT2.0L H4 16V GDI DOHCJF2GTAMC0K8356082S22-0526A15306.0
93722019FordEdge TitaniumUsed31985.04.7193.0DealerFord Lincoln of Smithtown4.71232.0440 Middle Country Rd St JamesNY11780Great4.84.74.74.64.84.7White PlatinumEbonyAll-wheel Drive21.028.0Gasoline8-Speed Automatic2.0L I4 16V GDI DOHC Turbo2FMPK4K99KBB49872U1324623016.0
93732019HondaCR-V EX-LUsed31999.04.8540.0DealerHoffman Honda4.81917.040 Albany Tpke West SimsburyCT06092Good4.84.84.74.74.84.8Obsidian Blue PearlNaNAll-wheel Drive27.033.0GasolineAutomatic CVT1.5L I4 16V GDI DOHC Turbo2HKRW2H87KH64304310553HB44481.0
93742019SubaruCrosstrek 2.0i PremiumUsed27374.04.7205.0DealerBertera Subaru of West Springfield4.4443.0657 Riverdale St. West SpringfieldMA01089Good4.74.74.64.74.84.8Quartz Blue PearlGrayAll-wheel Drive27.033.0GasolineAutomatic CVT2.0L H4 16V GDI DOHCJF2GTADC4KH318032220502A15606.0
93752019AudiQ8 3.0T PremiumUsed61998.04.827.0DealerAutobahn USA Westborough4.81789.088 Turnpike Road WestboroMA01581Fair4.94.84.84.65.04.9Night BlackBlackAll-wheel Drive17.022.0Hybrid8-Speed Automatic3.0L V6 24V GDI DOHC Turbo HybridWA1AVAF14KD015389AB471946855.0
93762017BuickEnclave LeatherUsed26944.04.8137.0DealerTulley Automotive Group4.7831.0147 Daniel Webster Hwy NashuaNH03060Good4.94.84.74.64.94.8Ebony Twilight MetallicEbonyAll-wheel Drive15.022.0Gasoline6-Speed Automatic3.6L V6 24V GDI DOHC5GAKVBKD4HJ190334B221381B62649.0
93772019SubaruForester PremiumUsed28568.04.7279.0DealerIra Subaru4.4680.097 Andover St DanversMA01923Good4.84.74.64.74.74.8Crystal Black SilicaBlackAll-wheel Drive26.033.0GasolineAutomatic CVT2.5L H4 16V GDI DOHCJF2SKAGC9KH423450KH42345030760.0
93782019HyundaiSanta Fe Ultimate 2.4Used32091.04.8204.0DealerRoute 44 Hyundai4.41105.01094 New State Hwy RaynhamMA02767Good4.94.94.64.84.94.8Twilight BlackBlackAll-wheel Drive21.027.0Gasoline8-Speed Automatic2.4L I4 16V GDI DOHC5NMS5CAD1KH002128H4834541645.0

Duplicate rows

Most frequently occurring

YearMakeModelUsed/NewPriceConsumerRatingConsumerReviewsSellerTypeSellerNameSellerRatingSellerReviewsStreetNameStateZipcodeDealTypeComfortRatingInteriorDesignRatingPerformanceRatingValueForMoneyRatingExteriorStylingRatingReliabilityRatingExteriorColorInteriorColorDrivetrainMinMPGMaxMPGFuelTypeTransmissionEngineVINStock#Mileage# duplicates
742017AudiQ7 3.0T Premium PlusUsed32499.04.772.0DealerBoise Volkswagen4.5251.08400 W. Franklin Rd. BoiseID83709Good4.94.84.74.44.74.6Glacier White MetallicRock GrayAll-wheel Drive19.025.0Gasoline8-Speed Automatic3.0L V6 24V GDI DOHC SuperchargedWA1LABF74HD021264P9090A81572.03
3662019HondaCR-V EX-LCertified33394.04.8540.0DealerSouth Tacoma Honda4.7545.07802 S Tacoma Way TacomaWA98409Good4.84.84.74.74.84.8Platinum White PearlBlackAll-wheel Drive27.033.0GasolineAutomatic CVT1.5L I4 16V GDI DOHC TurboJHLRW2H8XKX023259H16463B48627.03
4252019LexusES 350 F SportUsed42195.04.6121.0DealerJaguar Land Rover Dallas4.5309.011400 N Central Expy DallasTX75243Good4.74.64.64.44.74.6BlackRedFront-wheel Drive22.031.0Gasoline8-Speed Automatic3.5L V6 24V PDI DOHC58ABZ1B15KU0049581L3035A35760.03
4972019ToyotaCorolla LEUsed19002.04.8167.0DealerHertz Car Sales Orlando4.547.06060 S Semoran Blvd OrlandoFL32822Great4.84.74.74.74.84.9SilverWhiteFront-wheel Drive28.036.0GasolineAutomatic CVT1.8L I4 16V MPFI DOHC2T1BURHE0KC2027975536149651.03
5992020KiaSportage LXCertified26977.04.8174.0DealerSummit Place Kia Auburn Hills4.92015.04200 Interpark Dr Auburn HillsMI48326Good4.84.84.84.84.84.9Clear WhiteBlackAll-wheel Drive22.026.0Gasoline6-Speed Automatic2.4L I4 16V GDI DOHCKNDPMCAC7L7738001U1547137525.03
02004ToyotaHighlander BaseUsed5995.04.547.0DealerNorthtown Auto Sales4.92.08325 University Ave NE MinneapolisMN55432Good4.64.34.44.64.34.7TanTanFront-wheel Drive23.023.0GasolineAutomatic 5-Speed3.3L V6JTEGP21A74003999140039991STK204440.02
12005AcuraMDX TouringUsed6495.04.754.0DealerJP Motors4.37.07411 Centreville Rd ManassasVA20111Good4.84.54.64.84.64.9BlackEbonyFour-wheel Drive17.023.0Gasoline5-Speed Automatic3.5L V6 24V MPFI SOHC2HNYD188X5H5492562P771090174916.02
22007ToyotaCamry LEUsed9950.04.2301.0DealerTJK AUTO LLC4.110.014227 S Street OmahaNE68137Great4.44.14.14.24.34.3WhiteTanFront-wheel Drive22.031.0Gasoline6-Speed Automatic3.5L V6 24V MPFI DOHC4T1BK46K87U54891354891399223.02
32008Mercedes-BenzS-Class S 550 4MATICUsed15800.04.843.0DealerCar City Inc3.632.02232 N Rand Rd PalatineIL60074Great5.04.95.04.64.84.6BlueBrownAll-wheel Drive14.020.0Gasoline7-Speed Automatic5.5L V8 32V MPFI DOHCWDDNG86X58A227619464189247.02
42008Porsche911 CarreraUsed34995.04.841.0DealerMaximum Auto Outlet4.0409.08503 Euclid Ave Manassas ParkVA20111Great4.64.85.04.75.04.7Cobalt Blue MetallicNatural BrownAll-wheel Drive18.026.0GasolineAutomatic3.6L horizontally-opposed DOHC 24V 6-cyl engine-inc: dry sump luWP0CA29958S7658578S765857103956.02